/// <summary>
/// Takes 2 parent gene vectors, selects a midpoint and then swaps the ends
/// of each genome creating 2 new genomes which are stored in baby1 and baby2
/// </summary>
/// <param name="other"></param>
/// <param name="baby1"></param>
/// <param name="baby2"></param>
public void CrossOver(Genome <TMainModel, TCombinationModel> other, Genome <TMainModel, TCombinationModel> baby1, Genome <TMainModel, TCombinationModel> baby2)
{
// just return parents as offspring dependent on the rate
// or if parents are the same
if ((GAUtils.RandDouble() > _settings.CrossoverRate) || (this == other))
{
baby1.Genes = this.Genes;
baby2.Genes = other.Genes;
return;
}
if (baby1.Genes.Count <= 1 || baby2.Genes.Count <= 1)
{
baby1.Genes = this.Genes;
baby2.Genes = other.Genes;
return;
}
// determine a crossover point
int crossOverPoint = GAUtils.RandInt(0, this.Genes.Count - 1); // = 29
for (int i = 0; i < this.Genes.Count; i++)
{
var crossed = Genes[i].Cross(other.Genes[i], i < crossOverPoint);
baby1.Genes.Add(crossed[0]);
baby2.Genes.Add(crossed[1]);
}
}
public void Initialize()
{
int geneSize = _settings.InitialPopulation.Count; // number of all items to be combined
Parts.Clear(); // for re-initialisation when there is already a gene that's the same
for (int i = 0; i < geneSize; i++)
{
Parts.Add(new GenePart <TCombinationModel>
{
Model = _settings.InitialPopulation[i],
Active = GAUtils.RandDouble() > 0.5
});
}
}
/// <summary>
/// Iterates through each gene flipping the bits acording to the mutation rate
/// </summary>
public void Mutate()
{
// Go through each gene
for (int i = 0; i < Genes.Count; i++)
{
Gene <TCombinationModel> gene = Genes[i];
int mutationCounter = 0;
for (int j = 0; j < gene.Parts.Count; j++)
{
//do we flip this bit?
if (GAUtils.RandDouble() < _settings.MutationRate)
{
//flip the bit
gene.Parts[j].Active = gene.Parts[j].Active == false;
mutationCounter++;
}
}
//Debug.WriteLineIf(mutationCounter > 0, $"> Mutated {mutationCounter}/{gene.Parts.Count} items");
}
}
private Genome <TMainModel, TCombinationModel> RouletteWheelSelection()
{
double fSlice = GAUtils.RandDouble() * TotalFitnessScore;
double cfTotal = 0.0;
int selectedGenome = 0;
for (int i = 0; i < Genomes.Count; ++i)
{
cfTotal += Genomes[i].Fitness;
if (cfTotal > fSlice)
{
selectedGenome = i;
break;
}
}
return(Genomes[selectedGenome]);
}